Plasmodium infections, such as malarial sicknesses caused for example by the pathogens Plasmodium falciparum, Plasmodium vivax, Plasmodium ovale, Plasmodium malariae and also Plasmodium knowlesi, are the cause of hundreds of millions of new infections worldwide per year. According to figures from the World Health Organization (WHO) the estimate of new infections per year ranges from 300 to 500 million people.
In respect of the increasing development of resistance to the existing medicines for treating plasmodium infections there is therefore an increasing demand for reliable, low-cost and quick-to-perform diagnostic methods, which can additionally largely exclude false-positive and false-negative results. For example highly-sensitive diagnostic methods would be necessary in regions with a low prevalence of malarial infections in order to recognize the few people with the illness as actually having it.
On the other hand, in regions of the world with a higher prevalence/incidence, which are usually among the poorer regions of the world, there is a high demand for test methods with a high specificity, in order to exclude false-positive results (i.e. also recognize healthy people as being healthy).
As a rule the problem also exists of not only an extremely high number of patient samples having to be examined, but of this—as discussed above—also having to be done quickly. This is because the results of the diagnosis should be available within less than 2 hours. If this is not possible the patient may possibly have to be treated on the basis of a superficial clinical analysis.
The results of an incorrect or false-negative/false-positive malaria diagnosis are wide-ranging: with a false-positive diagnosis the (pointless) use of medicines can be accompanied by avoidable side-effects, quite apart from the financial load on the healthcare system, as well as the possibility of the plasmodia building up resistance. In a study from the year 2006 it was concluded that a diagnostic test with the sensitivity and specificity of 95% in each case which only needs minimal infrastructure could save more than 100,000 deaths and more than 400 million unnecessary treatments (Rafael M E, Taylor T, Magill A et al. Reducing the burden of childhood malaria in Africa: The role of improved diagnosis. Nature. 2006; 444 (suppl 1): 39-48)
In practice the main problem arising in the diagnosis of parasitic illnesses, for example malaria, is that the laboratory diagnosis is only undertaken when a clinical suspicion arises that the patient is actually suffering from this type of infection. This can lead, especially in regions with a low prevalence/incidence, to persons with the illness not being treated or not being treated in good time. For example in a Canadian study (Kain et al, 1998, Clinics in Infectious Diseases 27, 142-149) it was reported that the correct diagnosis was not initially made for 59% of all travelers returning infected with malaria. On average 7.6 days elapsed before the correct diagnosis was made and before the beginning of therapy for Plasmodium falciparum and 5.1 days for Plasmodium vivax. These types of delays can lead to significant complications and to an increased mortality rate (Humare et al, 1997, Canadian Medical Association Journal 156, 1165-1167).
There are a number of methods available for the diagnosis of plasmodium infections: the safest method consists of a microscopic blood examination, however this method is very labor-, time- and equipment-intensive. With the conventional microscopic method trained expert personnel can reliably determine the type and the stage of the infection.
So-called Rapid Diagnostic Tests (RDT) also exist. For example monoclonal antibodies are used here for the verification of parasitic antigens. This test is usually used to detect Plasmodium falciparum infections.
A far more sensitive method for malaria diagnosis consists of the polymerase chain reaction which, because of the high outlay in materials and time however, is little suited to acute cases.
The group of Rapid Diagnostic Tests (RDT) has recently also come to include automated methods which, because of their high throughput, are outstandingly suitable for wide-area-coverage verification methods. Cf. Hanscheid T, Pinto B G, Pereira I, Christino J M, Valadas E (1999) Avoiding misdiagnosis of malaria: a novel automated method allows specific diagnosis, even in the absence of clinical suspicion. Emerging Infectious diseases [1999, 5(6): 836-838].
For automatic use so-called automated cell counters are being employed with increasing success. Examples of such counters are the Advia 2120, Sysmex XE-2100 and also CellaVision DM96. These automated devices, apart from their high throughput rate, provide a number of benefits, such as for example higher objectivity (no observer-dependent variability), elimination of statistical variations which are usually associated with manual counting (counting high cell numbers), as well as the determination of numerous parameters which would not be available during manual counting and, as mentioned, a more efficient and cost-effective treatment. A few of these devices can process between 120 and 150 patient samples per hour.
The technical principles of the automatic single cell counting are based either on an impedance measurement or on an optical system (scattered light or absorption measurement).
With the impedance method the counting of the cells and also the determination of their size is done on the basis of the detection and the measurement of changes in the electrical conductivity (impedance) which are caused by a particle which is moving through a small opening. Particles, such as blood cells for example, are not themselves conductive, but are suspended in an electrically-conductive thinning medium. If such a suspension of cells is passed through an opening, during the passage of a single individual cell the impedance of the electrical path between the two electrodes which are located on each side of the opening temporarily increases.
For example a method is described in WO 2005/088301 for verifying malaria and other parasitic infections by means of such an impedance measurement (see experimental part) and also in the article entitled “Development of an Automated Malaria Discriminant Factor Using VCS Technology”, Briggs C et al. Am J Clin Pathol 2006.
By contrast with the impedance methods, the optical method comprises the passing of a laser light beam through a thinned blood sample which is detected in a continuous stream by the laser beam. Each cell which passes through the detection zone of the throughflow cells scatters the focused light. The scattered light is then detected by a photo detector and converted into an electrical impulse. The number of impulses generated here is directly proportional to the number of cells which pass through the detection zone within a specific period of time.
In the optical methods the light scattering of the individual cells which pass through the detection zone is measured at different angles. Information about cell structure, shape and reflection ability is detected through this. These properties can be used to differentiate between different types of blood cells and use the derived parameters for diagnosis of deviations of the blood cells from the norm.
The values obtained by the two measurement methods are logically linked by means of differential diagnostics into a meaningful diagnosis result.
The sensitivity and specificity of diagnostic methods plays a major role within the framework of differential diagnostics, accordingly work on improving these properties is constantly being undertaken.
For example in WO 2005/088301 the measured values relating to the cell volume of lymphocytes and monocytes are detected and included as parameters for a malarial disease. In more precise terms the standard deviation of the volumes of the monocyte and lymphocyte populations is assessed, i.e. their heterogeneity. It has transpired however that this parameter is not specific enough for the diagnosis of a parasitic infection, since other infectious diseases (for example colds) can also result in a change in volume of the lymphocytes and monocytes. In addition impedance measurements of blood samples have also proved to be susceptible to errors, e.g. measurement results can be falsified (for example by varying viscosity of the suspension to be tested).
In a similar manner a description is given in “Development of Automated Malaria Discriminant Factor Using VCS Technology” (see above) of how the standard deviation of the volume of lymphocytes and monocytes deviates significantly from the standard value when a malaria infection is present.
An example presentation of different sensitivities and specificities using automated blood test devices can be found in Table 3, in accordance with which the sensitivity in some cases only amounts to 48.6 or 52%. Success is thus not achieved to a sufficient extent with the conventional test methods in providing methods with high sensitivity and specificity, i.e. methods for detecting a plasmodium infection, which also recognize ill patients as such, as well as on the other hand being able to recognize a healthy patient as healthy.
In accordance with more recent investigations the expressiveness of the existing automated test systems (and thus also of the specified sensitivities/specificities) is also questionable and allows scope for improvements. In this connection see the recently published paper “Automated hematology analysis to diagnose malaria”, Malaria Journal 2010, 9:346.